OSCR

Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease.

Code ↔ Paper

18 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 18 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Neurotransmitter receptor gene expression ↔ AHBA_gene_expression.py, lines 73–91 · score 0.84 · nulls.burt2020, spatial autocorrelation preserving, gene expression, nodal FS, permutations, seed
  2. [2] § Methods › The Weighted Eigenvector Dynamics Analysis (WEiDA) and identification of metastable brain states ↔ pyleida/_leida.py, lines 194–252 · score 0.79 · instantaneous phase coherence, phase coherence matrix, LEiDA, leading eigenvector, fMRI, volume
  3. [3] § Methods › The Weighted Eigenvector Dynamics Analysis (WEiDA) and identification of metastable brain states ↔ StateSpace_modelling.m, lines 51–122 · score 0.79 · Hilbert transformed, BOLD signal, weighted eigenvector, 0.07 Hz, 0.04 Hz, filtered
  4. [4] § Methods › The Weighted Eigenvector Dynamics Analysis (WEiDA) and identification of metastable brain states ↔ MSanalysis.m, lines 68–135 · score 0.78 · Hilbert transformed, BOLD signal, weighted eigenvector, 0.07 Hz, 0.04 Hz, filtered
  5. [5] § Methods › Sensitivity analysis ↔ across_atlas.py, lines 43–71 · score 0.76 · MNI152 space, topographic correlations, spatial autocorrelation, Schaefer, WEiDA, AAL
  6. [6] § Methods › Sensitivity analysis ↔ plot_fig.ipynb, lines 431–461 · score 0.67 · fsaverage5 space, metastable state, atlases, Schaefer, WEiDA, AAL
  7. [7] § Results › Probabilistic metastable substates identified by WEiDA ↔ pyleida/_leida.py, lines 194–252 · score 0.67 · phase coherence matrices, LEiDA, Leading eigenvector, fMRI, probabilistic, transitions
  8. [8] § Results › Exploratory associations between neuromodulation-induced functional segregation and PD-related gene expressions ↔ plot_fig.ipynb, lines 345–394 · score 0.63 · gene expression, GLUD1, GLUD2, GLUL, glutamatergic, rho
  9. [9] § Methods › Sensitivity analysis ↔ pyleida/clustering/_clustering.py, lines 250–374 · score 0.61 · Davies Bouldin, Silhouette score, clustering, eigenvector
  10. [10] § Methods › Neurotransmitter receptor gene expression ↔ pyleida/plotting/_plotting.py, lines 296–427 · score 0.60 · right hemispheres, Cortical surface, threshold, parcellation, background, mapped
  11. [11] § Methods › Functional profiles for metastable brain states ↔ pyleida/clustering/rsnets_overlap.py, lines 11–116 · score 0.60 · resting state networks, correlation coefficient, cluster centroid, RSNs, vectors, overlap
  12. [12] § Results › STN-tTIS elicits metastable dynamics that are akin to those induced by STN-DBS ↔ AHBA_gene_expression.py, lines 73–91 · score 0.58 · spatial autocorrelation preserving, nodal FS, gene expression, Spearman, model, metrics
  13. [13] § Results › Robustness, consistency and reliability of WEiDA ↔ across_atlas.py, lines 43–71 · score 0.58 · spatial autocorrelation preserving, topographic correlations, Schaefer, WEiDA, AAL, model
  14. [14] § Results › STN-tTIS elicits metastable dynamics that are akin to those induced by STN-DBS ↔ WEiDA_pred.m, lines 8–41 · score 0.55 · state fractional occupancy, state space, tTIS, PRE, probability, transition
  15. [15] § Results › Exploratory associations between neuromodulation-induced functional segregation and PD-related gene expressions ↔ AHBA_gene_expression.py, lines 50–57 · score 0.54 · gene expression, GLUD1, GLUD2, GLUL, AHBA, glutamatergic
  16. [16] § Methods › Functional profiles for metastable brain states ↔ overlapRSN.m, the whole file · a weak match · score 0.54 · MNI space, RSNs, cluster centroid, overlap, AAL, Spearman
  17. [17] § Methods › Neurotransmitter receptor gene expression ↔ plot_fig.ipynb, lines 345–394 · score 0.54 · PD related gene, gene expression, glutamatergic, dopaminergic, Atlas
  18. [18] § Methods › The Weighted Eigenvector Dynamics Analysis (WEiDA) and identification of metastable brain states ↔ WEiDA_pred.m, lines 8–41 · score 0.50 · state space, tTIS, cluster centroid, brain state, PRE, Weighted

Paper

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The authors' code

Python · 93 lines · 3.5 KB · no license · 3 matches

  1. import abagen
  2. import pandas as pd
  3. import scipy.stats as stats
  4. import numpy as np
  5. import nibabel as nib
  6. import copy
  7. from neuromaps import nulls
  8. from neuromaps.stats import compare_images
  10. files = abagen.fetch_microarray(donors='all', data_dir='./Image_data/abagen-data/microarray')
  12. # remove subcortical areas from AAL1
  13. img_org=nib.load("./data/atlas/AAL1_MNI.nii.gz")
  14. img = img_org.get_fdata()
  15. all_labels = np.unique(img)
  17. check_img = copy.copy(img)
  18. for i in [37,38,41,42,71,72,73,74,75,76,77,78]:
  19. check_img[img==all_labels[i]] = 0
  20. for i in range(91,117):
  21. check_img[img==all_labels[i]] = 0
  23. check_all_labels = np.unique(check_img)
  25. imga = nib.Nifti1Image(check_img, img_org.affine)
  26. nib.save(imga, "./data/atlas/AAL78_MNI.nii.gz")
  28. # Organize Lookuptable
  29. org_info = pd.read_csv("./data/atlas/AAL1_MNI.csv")
  30. idx=[36,37,40,41,70,71,72,73,74,75,76,77]
  31. for i in range(90,116):
  32. idx.append(i)
  33. info=org_info.drop(index=idx).reset_index(drop=True)
  35. info['Index'] = info['Intensity']
  36. info = info.drop(columns=['Space','Intensity'])
  37. info.columns=['id','label','hemisphere']
  38. info['structure']='cortex'
  40. info.to_csv("./data/atlas/abagen_AAL78_Lookuptable.csv", index=False)
  42. # gene expression projected on AAL1 cortical atlas (with interpolation by nearest centroids)
  43. atlas_AAL78 = {'image':"./data/atlas/AAL78_MNI.nii.gz", \
  44. 'info':"./data/atlas/abagen_AAL78_Lookuptable.csv"}
  45. abagen.images.check_atlas(atlas_AAL78['image'], atlas_AAL78['info'])
  46. expression = abagen.get_expression_data(atlas_AAL78['image'], atlas_AAL78['info'], missing='centroids')
  48. expression.to_csv("./data/atlas/AAL78_expression_centroids.csv")
  50. # Define Transmitters into lists
  51. acetylcholine = ["CHRM1", "CHRM2", "CHRM3", "CHRM4", "CHRM5", "CHRNA2", "CHRNA3", "CHRNA4", "CHRNA6", "CHRNA7", "CHRNA10", "CHRNB1", "CHRNB2"]
  52. dopamine = ["DRD1", "DRD2", "DRD4"]
  53. G_aminobutyric_acid = ['GABARAP','GABARAPL1','GABARAPL2','GABARAPL3']
  54. glutamate=['GLUD1','GLUD2','GLUL']
  55. # Select and save transmitters only
  56. allTransmitter = G_aminobutyric_acid + acetylcholine + dopamine + glutamate
  57. expression = expression[allTransmitter]
  59. # generate nodal-FS T-map aross subject groups
  60. nodal_fs = pd.read_csv("./WEiDA4_atlasAAL78_tTIS/nodal_FS.csv", index_col=0)
  61. num_parcel=78
  62. t = [None] * num_parcel
  63. for i in range(num_parcel):
  64. t[i], p = stats.ttest_rel(nodal_fs.iloc[11:22,i], nodal_fs.iloc[0:11,i])
  65. expression["nodal_FS_T_stat"] = t
  67. expression.to_csv("./WEiDA4_atlasAAL78_tTIS/Transmitter_nodalFS_Tmap.csv")
  69. # topographic correlations between the nodal-FS T map and gene expression maps
  70. fs_parc = np.array(expression["nodal_FS_T_stat"])
  71. parcellation = nib.load('./data/atlas/AAL78_MNI.nii.gz')
  73. # spatial autocorrelation-preserving null model
  74. rotated = nulls.burt2020(fs_parc, atlas='MNI152', density='2mm',
  75. n_perm=10000, seed=3512, parcellation=parcellation)
  76. df_rotated = pd.DataFrame(rotated)
  77. df_rotated.to_csv("./WEiDA4_atlasAAL78_tTIS/Rotated3512_MNIparc78_perm10k_FStmap.csv")
  79. rho = np.zeros(len(allTransmitter))
  80. pvals = np.zeros(len(allTransmitter))
  81. for j in range(len(allTransmitter)):
  82. gene_parc = np.array(expression[allTransmitter[j]])
  83. corr, pval = compare_images(fs_parc, gene_parc, nulls=rotated, metric='spearmanr')
  84. print(f'r = {corr:.3f}, p = {pval:.3f}')
  85. rho[j] = corr
  86. pvals[j] = pval
  88. exportData = pd.DataFrame({'Gene':allTransmitter})
  89. exportData["rho"] = rho
  90. exportData["pValue"] = pvals
  91. exportData.to_csv("./WEiDA4_atlasAAL78_tTIS/Transmitter_nodalFS_spacorr_MNIparc78_perm10k_spearmanr.csv")

AHBA_gene_expression.py at commit 2e20b0b, no license · at the source

Overview

Authors: Chenfei Ye1, Chen Ran2, Yongxin Xu3, Chunguang Chu4, Chenhao Yang3, Chencheng Zhang5, Yu Liu3, Ting Ma1
  1. School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
  2. Department of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
  3. Key Laboratory of Exercise and Health Sciences of Ministry of Education, School of Exercise and Health, Shanghai University of Sport, Shanghai, China
  4. Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  5. Department of Neurosurgery, Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Institutions: Harbin Institute of Technology (China); Shanghai University of Sport (China); Shanghai Jiao Tong University (China); Ruijin Hospital (China)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 150
Dates: received 15 August 2025; accepted 2 April 2026; published online 20 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41531-026-01354-3 · PMID 42009672 · PMCID PMC13280393 · OpenAlex W7155003976
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other condition (population), Parkinson's (population)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, Preprocessing, fMRI & imaging
Keywords: Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of P.R. China (62276081, 62106113); Guangdong Basic and Applied Basic Research Foundation (2023A1515010792, 2023B1515120065); Shenzhen Science and Technology Program (GXWD20231129121139001, JCYJ20240813110522029); National Key Research and Development Program of China (2021YFC2501202)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

PSYMARKER/leida-python

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 0f06c2713795eb05584c5436151e25637f35aed4, 23 September 2022
Languages: Python (19)
Size: 43 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (environment.yml, setup.py), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (11 files), Matplotlib (9 files), pandas (9 files), seaborn (9 files), SciPy (4 files), Nilearn (3 files), imageio (2 files), scikit-learn (2 files), NiBabel (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
21 files
At the source: github.com/PSYMARKER/leida-python

chenfei-ye/WEIDA-DBS

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 2e20b0bc956e6eb0eb8e06e2aaf00a93b014e72c, 21 April 2026
Languages: MATLAB (11), Python (5), Jupyter (1)
Size: 18 files, 17 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), pandas (6 files), SciPy (5 files), Signal Processing Toolbox (3 files), NiBabel (3 files), neuromaps (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), statsmodels (2 files), abagen (1 file), BrainSpace (1 file), Statistics and Machine Learning Toolbox (1 file), Matplotlib (1 file), Nilearn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
18 files
At the source: github.com/chenfei-ye/WEIDA-DBS

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41531-026-01354-3.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 36 scripts, each with its path and the digest of its content;
  • 18 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41531-026-01354-3.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 4 funders, 96 references.

Cite

This paper

Ye, C., Ran, C., Xu, Y., Chu, C., Yang, C., Zhang, C., Liu, Y., & Ma, T. (2026). Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease. NPJ Parkinson's disease, 12(1), 150. https://doi.org/10.1038/s41531-026-01354-3

BibTeX

@article{ye2026neuromodulation,
author = {Ye, Chenfei and Ran, Chen and Xu, Yongxin and Chu, Chunguang and Yang, Chenhao and Zhang, Chencheng and Liu, Yu and Ma, Ting},
title = {{Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = apr,
volume = {12},
number = {1},
pages = {150},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01354-3},
url = {https://doi.org/10.1038/s41531-026-01354-3},
pmid = {42009672},
pmcid = {PMC13280393}
}

RIS

TY - JOUR
AU - Ye, Chenfei
AU - Ran, Chen
AU - Xu, Yongxin
AU - Chu, Chunguang
AU - Yang, Chenhao
AU - Zhang, Chencheng
AU - Liu, Yu
AU - Ma, Ting
TI - Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/04/20
VL - 12
IS - 1
SP - 150
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01354-3
UR - https://doi.org/10.1038/s41531-026-01354-3
LA - en
ER -

CSL-JSON

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